Preparation and characterization of photo-stimuli-responsive fibers based on lanthanide-activated phosphors and spiropyran dye
Bibliographic record
Abstract
Stimuli-responsive fibers are highly desirable for their potential applications in areas such as anti-counterfeiting, optical memory systems, colorimetric sensing, and bioimaging sensors. An iridescent luminescent fibers that can achieve multi-color with long afterglow and photochromic properties is always required in color-on-demand applications. Here, a set of distinct three-primary colors luminescent fibers based on red-green-blue (RGB) system. Y2O2S:Eu3+, Mg2+, Ti4+ (red), SrAl2O4:Eu2+, Dy3+ (green), Sr2MgSi2O7: Eu2+, Dy3+ (blue) are successfully synthesized. Spiropyran and as-prepared luminescent materials are introduced into PAN fibers by a one-step extrusion wet-spinning process to achieve distinct three-primary colors luminescent photochromic fiber. These as-prepared fibers emit bright lights under excitation of ultraviolet with long afterglow. These fibers exhibit outstanding tensile properties which allow knitting or weaving them into demonstrated functional designs for aesthetic designs and encryption/decryption protocols. Comparing with previous studied stimuli-responsive fibers, our research focus on distinct three-primary colors luminescent photochromic fibers present enormous potential in 3D printing, information encryption, and next generation smart fibers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".